How far into the future should policymakers specify the GHG emissions targets they and their nations propose? How far into the future should climate modelers try to forecast the consequences of those policies? At the UNFCCC meeting in Bonn we observed mixed opinions on these questions, specifically whether, in calculating the gap between current pledges and the ability to meet climate goals, modelers and analysts should focus their attention only on near term targets (such as 2020) or whether we should be providing decision makers longer-term assessments of the gap as well. — Beth Sawin
Short Time Horizons Lead to Shortsighted Decisions
Many nations have provided commitments under the Copenhagen Accord for emissions only through 2020, and some analytical groups have calculated total emissions only through this year, arguing it is speculative to go further. However, it is neither good policy nor good science to calculate impacts only to 2020 or even 2050, when the impacts of emissions spill out not only through 2100 but for centuries beyond. Climate models consistently show that the impacts of past and projected emissions will continue for centuries, and, in the case of sea level rise, likely for millennia. The parties should provide estimates of, if not commitments for, their post-2020 emissions, and analytical groups should also be demanding this information.
Short time horizons lead to shortsighted decisions. In my textbook (Business Dynamics) I provide a cautionary example:
“The choice of time horizon can dramatically influence the evaluation of policies. In the early 1970s a US government agency concerned with foreign aid sponsored a model focused on the Sahel region of sub-Saharan Africa. The Sahel was then experiencing rapid population growth at the same time the desert was expanding southward, reducing grazing land for the nomadic herders’ cattle. The purpose of the model was to identify high leverage policies to spur economic development in the region. The model was used to assess the effects of many of the policies then in use, such as drilling bore holes to increase the water supply for cattle by tapping deep aquifers or subsidizing crops such as sorghum and ground nuts. Running the model to the year 2000, a round number several decades in the future at the time, showed that the policies led to improvement. Subsidies increased agricultural output. Bore holes permitted cattle stocks to grow, increasing the supply of milk and meat and the wealth of the herders. However, running the model into the first decades of the 21st century showed a different outcome: larger stocks of cattle began to outstrip the carrying capacity of the region. As the cattle overbrowsed and trampled the grasslands, erosion and desertification increased. The cattle population dropped sharply, creating a food deficit in the region. Selecting a time horizon too short to capture these feedbacks favored adoption of policies counter to the long-term interests of the region’s people and the mission of the client organization.” (Sterman, J (2000) Business Dynamics. p. 94)
What I didn’t write in the book is that, consistent with good practice and honest inquiry, the modelers originally ran their model through 2020, long enough for the full impact of the policies the client organization was promoting to be captured. However, when the managers of the client organization saw that these results made the policies they were following look bad, they demanded that the results only be displayed through 2000, hiding the harmful long-run consequences of their policies.
We can’t afford such blinders in climate change policy and we mustn’t let the parties or other modeling and analytics groups hide the fact that current commitments to emissions reductions are insufficient to stabilize GHG concentrations at all, much less at any reasonable level that would limit the risks of dangerous anthropogenic interference in the climate system.